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Youtube Comment Analyzer →
This platform enables users to host events and buy/sell tickets online. There are two types of users: event hosts and normal users. Event hosts can manage events and ticketing, while normal users can purchase tickets and access events seamlessly.
Challenge
Normal users struggle to receive updates about relevant events. Event hosts face difficulty providing tickets with zero platform fees. Managing event attendees is often cumbersome for hosts.
Objective
To develop an all-in-one mobile application for seamless event hosting and ticket booking.
Solution
A user-centric mobile application was designed and developed to address these challenges, enabling individuals to easily host events and book tickets.
Result
The platform recommends five top events to users based on their preferences, location, and popular community recommendations.
Conclusion
This application bridges the gap between event hosts and users by offering a smooth and cost-effective ticketing solution, enhancing event experiences for both parties.
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The "YouTube Comment Analyzer" is a tool designed to extract and analyze comments from YouTube videos. By leveraging Large Language Models (LLMs), it categorizes comments into various types—such as positive feedback, negative feedback, suggestions, and requests—to provide content creators with a comprehensive understanding of audience engagement.
Challenge
YouTube content creators often face the challenge of sifting through thousands of comments to gauge audience sentiment and gather actionable feedback. Manually analyzing such a vast amount of data is time-consuming and may lead to overlooked insights.
Objective
The primary objective of this project is to automate the collection and analysis of YouTube comments, enabling creators to efficiently assess audience reactions and identify areas for improvement.
Solution
- Input: Users provide a YouTube video link.
- Comment Collection: The tool retrieves comments from the specified video.
- LLM Analysis: Each comment is analyzed using a Large Language Model to determine its context and sentiment.
- Categorization: Comments are sorted into categories such as positive feedback, negative feedback, suggestions, and requests.
- Report Generation: A comprehensive report is generated, detailing the analysis and categorization of comments.
- The tool supports analyzing between 5,000 to 70,000 comments within minutes and ensures accurate categorization through the use of LLMs. The generated report is saved to a file for further review.
Result
By utilizing this analyzer, content creators can quickly gain insights into audience sentiment, identify common suggestions or requests, and understand areas that may require attention. This streamlined analysis aids in making informed decisions to enhance content quality and audience engagement.
Conclusion
The YouTube Comment Analyzer effectively addresses the challenges of manual comment analysis by providing an automated, efficient, and accurate method to assess audience feedback. Its integration of LLMs for sentiment analysis and categorization offers creators valuable insights, ultimately contributing to improved content strategies and viewer satisfaction.
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What Our clients Says About Us
Our clients' experiences speak volumes about our dedication and quality of service. We are proud to share some of their feedback with you.
Their team not only designed a visually stunning site but also optimized it for performance and SEO. The project was delivered on time, and their support has been outstanding. Highly recommended
Sanika Prasad
CEO, Amul